Polymorphic variation in genes regulating estrogen synthesis, bioavailability and metabolism may contribute to the individual risk for breast cancer. Few previous studies had statistical power to examine complex interactions among these genes. Moreover, the majority of previous (mostly null) studies considered only single genetic markers in candidate genes with the possibility that important relationships were not detected. In this application we are proposing to study the contribution of variation in the estrogen genes to breast cancer, using existing data and materials collected in the Collaborative Breast Cancer Study, a large, population-based case-control study conducted in the US. Over a 4-year period (1997-2001), more than 4,400 women with a recent diagnosis of breast cancer and 3,800 population-based controls completed a telephone interview on breast cancer risk factors and provided buccal mucosal DNA for genetic research. Response rates in cases (72%) and controls (63%) were high for this type of research, and the average yield of DNA was adequate for typing several hundred variants. All of the anonymized samples have been extracted for DNA, aliquoted and stored at -70 x for this planned research. We are now proposing to test current hypotheses linking critical genes in estrogen biosynthesis and metabolism to the risk for breast cancer. Genes included in this proposal are involved at key branch points in estrogen synthesis (STAR, CYP11A1, HSD 3B, CYP17, CYP19, HSD17B , TNF, IL6, PPAR G, STS), in steroid signaling (ESR1, ESR2, PGR, SHBG, AIB1), and in estrogen metabolism (CYP1A1, CYP1A2, CYPIB1, CYP3A4) and inactivation (SULTs, UGTs, COMT, NQOs, GSTs). Using the best available information, we propose to study both known functional variants, and also single nucleotide polymorphisms in other unlinked regions of genes to increase the chance of detecting associations. Several of the genes are novel to this application, and we propose detailed genetic analyses using CEPH pedigrees to determine haplotype structures in this population. Interactions among variants will be tested using statistical procedures designed to detect high-order genetic interaction. This is one of the largest assembled resources of DNA and epidemiologic data for research in breast cancer. The work proposed in this application will provide timely and cost-effective new information on the genetic pathways to breast cancer that may prove relevant to screening, detection, and more targeted treatment strategies.